Abstract
This paper deals with the problem of autoscaling for cloud computing scientific workflows. Autoscaling is a process in which the infrastructure scaling (i.e. determining the number and type of instances to acquire for executing an application) interleaves with the scheduling of tasks for reducing time and monetary cost of executions. This work proposes a novel strategy called Spots Instances Aware Autoscaling (SIAA) designed for the optimized execution of scientific workflow applications. SIAA takes advantage of the better prices of Amazon’s EC2-like spot instances to achieve better performance and cost savings. To deal with execution efficiency, SIAA uses a novel heuristic scheduling algorithm to optimize workflow makespan and reduce the effect of tasks failures that may occur by the use of spot instances. Experiments were carried out using several types of real-world scientific workflows. Results demonstrated that SIAA is able to greatly overcome the performance of state-of-the-art autoscaling mechanisms in terms of makespan (up to 88.0%) and cost of execution (up to 43.6%).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agmon Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., Tsafrir, D.: Deconstructing amazon EC2 spot instance pricing. ACM T. Econ. Comput. 1(3), 16 (2013)
Amazon: Amazon Auto Scaling, http://aws.amazon.com/autoscaling/ (June 2014) (Online accessed June 24, 2014)
Amazon: EC2 spot instances (June 2014), http://aws.amazon.com/ec2/purchasing-options/spot-instances/ (Online accessed June 24, 2014)
Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comp. Sy. 25(6), 599–616 (2009)
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software Pract. Exper. 41(1), 23–50 (2011)
Iosup, A., Yigitbasi, N., Epema, D.: On the performance variability of production cloud services, pp. 104–113 (May 2011)
Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future Gener. Comp. Sy. 29(3), 682–692 (2013)
Mao, M., Humphrey, M.: A performance study on the vm startup time in the cloud. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 423–430. IEEE (2012)
Mao, M., Humphrey, M.: Scaling and scheduling to maximize application performance within budget constraints in cloud workflows. In: 2013 IEEE 27th International Symposium on Parallel & Distributed Processing (IPDPS), pp. 67–78. IEEE (2013)
Pllana, S., Brandic, I., Benkner, S.: A survey of the state of the art in performance modeling and prediction of parallel and distributed computing systems. Int. J. Comput. Int. Sys. Res. 4(1), 279–284 (2008), http://eprints.cs.univie.ac.at/326/
Rahman, M., Hassan, R., Ranjan, R., Buyya, R.: Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurr. Comp. Pract. E 25(13), 1816–1842 (2013)
Schad, J., Dittrich, J., Quiané-Ruiz, J.A.: Runtime measurements in the cloud: Observing, analyzing, and reducing variance. Proc. VLDB Endow. 3(1-2), 460–471 (2010), http://dx.doi.org/10.14778/1920841.1920902
Taylor, I., Deelman, E., Gannon, D., Shields, M.: Workflows for e-Science: Scientific Workflows for Grids, 1st edn. Springer, London (2007)
Voorsluys, W., Buyya, R.: Reliable provisioning of spot instances for compute-intensive applications. In: 2012 IEEE 26th International Conference on Advanced Information Networking and Applications (AINA), pp. 542–549. IEEE (2012)
Wallace, R., Turchenko, V., Sheikhalishahi, M., Turchenko, I., Shults, V., Vazquez-Poletti, J., Grandinetti, L.: Applications of neural-based spot market prediction for cloud computing. In: 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), vol. 2, pp. 710–716 (September 2013)
Yi, S., Andrzejak, A., Kondo, D.: Monetary cost-aware checkpointing and migration on amazon cloud spot instances. IEEE Transactions on Services Computing 5(4), 512–524 (2012)
Zhu, M., Wu, Q., Zhao, Y.: A cost-effective scheduling algorithm for scientific workflows in clouds. In: 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC), pp. 256–265 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Monge, D.A., García Garino, C. (2014). Adaptive Spot-Instances Aware Autoscaling for Scientific Workflows on the Cloud. In: Hernández, G., et al. High Performance Computing. CARLA 2014. Communications in Computer and Information Science, vol 485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45483-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-45483-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45482-4
Online ISBN: 978-3-662-45483-1
eBook Packages: Computer ScienceComputer Science (R0)